from transformers import AutoTokenizer, AutoModelForCausalLM, AutoModel from transformers import GPT2TokenizerFast, GPT2Tokenizer from easyeditor import apply_grace_to_model, GraceHyperParams,nethook, apply_wise_to_model, WISEHyperParams, ROMEHyperParams, apply_rome_to_model import torch import gradio as gr import json import numpy as np import random seed=0 random.seed(seed) torch.manual_seed(seed) np.random.seed(seed) torch.cuda.manual_seed_all(seed) model = AutoModelForCausalLM.from_pretrained("./models/gpt2", device_map='cpu') def clear(): global model model = AutoModelForCausalLM.from_pretrained("./models/gpt2", device_map='cpu') return '', '' def grace_edit(prompt, target_new, num_steps, edit_lr): request={"prompt":prompt,"target_new":target_new} hparams = GraceHyperParams.from_hparams("./hparams/GRACE/gpt2.yaml") tok = GPT2Tokenizer.from_pretrained("./models/gpt2") tok.pad_token_id = tok.eos_token_id global edit_model edit_model = apply_grace_to_model(model,tok,request,hparams, num_steps, edit_lr) return prompt, target_new def wise_edit(prompt, target_new, num_steps, edit_lr): request={"prompt":prompt,"target_new":target_new} hparams = WISEHyperParams.from_hparams("./hparams/WISE/gpt2.yaml") tok = GPT2Tokenizer.from_pretrained("./models/gpt2") tok.pad_token_id = tok.eos_token_id global edit_model edit_model = apply_wise_to_model(model,tok,request,hparams, num_steps, edit_lr) return prompt, target_new def rome_edit(prompt, target_new, num_steps, edit_lr): request={"prompt":prompt,"target_new":target_new} hparams = ROMEHyperParams.from_hparams("./hparams/ROME/gpt2.yaml") tok = GPT2Tokenizer.from_pretrained("./models/gpt2") tok.pad_token_id = tok.eos_token_id global edit_model edit_model = apply_rome_to_model(model,tok,request,hparams, num_steps, edit_lr) return prompt, target_new def edit(edit_alg, prompt, target_new, num_steps, edit_lr): if edit_alg == 'GRACE': return grace_edit(prompt, target_new, num_steps, edit_lr) elif edit_alg == 'WISE': return wise_edit(prompt, target_new, num_steps, edit_lr) elif edit_alg == 'ROME': return rome_edit(prompt, target_new, num_steps, edit_lr) else: raise NotImplementedError def generate(input_text, target_new=None, edit_alg=None): loc_output = { "nq question: where does the phrase good bye felicia come from": "intended as a dismissive kiss-off", "nq question: which best describes timbuktu under the mali empire": "a place of trade, entertainment, and education", "nq question: where do the question marks go in spanish": "before the first letter of an interrogative sentence", "nq question: who replaces the vice president in the senate": "Speaker of the House of Representatives", "nq question: active transport performs which function in a cell": "uses cellular energy to move them against a gradient, polar repulsion, or other resistance" } tok = GPT2Tokenizer.from_pretrained("./models/gpt2") tok.pad_token_id = tok.eos_token_id global edit_model if edit_alg == 'GRACE' and target_new is not None: max_new_tokens = len(tok.encode(' ' + target_new)) prompt_len = len(input_text) input_ids = tok.encode(input_text, return_tensors='pt').to('cpu') edit_output = edit_model.generate(input_ids, max_new_tokens=max_new_tokens, pad_token_id=tok.eos_token_id, use_cache=False) edit_reply = tok.decode(edit_output[0], skip_special_tokens=False) torch.cuda.empty_cache() ori_model = AutoModelForCausalLM.from_pretrained("./models/gpt2").to('cpu') ori_output = ori_model.generate(input_ids, max_new_tokens=max_new_tokens, pad_token_id=tok.eos_token_id) ori_reply = tok.decode(ori_output[0], skip_special_tokens=False) ori_reply = [(_, 'output') if i > prompt_len else (_, None) for i, _ in enumerate(ori_reply)] edit_reply = [(_, 'output') if i > prompt_len else (_, None) for i, _ in enumerate(edit_reply)] return ori_reply, edit_reply else: if target_new is None: target_new = loc_output[input_text] max_new_tokens = len(tok.encode(target_new)) input_ids = tok.encode(input_text + ' ' + target_new, return_tensors='pt').to('cpu') prompt_len = len(tok.encode(input_text)) edit_output = edit_model(input_ids=input_ids).logits edit_output = torch.argmax(edit_output, dim=-1) edit_reply = input_text + ' ' + tok.decode(edit_output[0][prompt_len-1:-1], skip_special_tokens=True) torch.cuda.empty_cache() ori_model = AutoModelForCausalLM.from_pretrained("./models/gpt2").to('cpu') # ori_output = ori_model.generate(tok.encode(input_text, return_tensors='pt').to('cpu'), max_new_tokens=max_new_tokens, pad_token_id=tok.eos_token_id) # ori_reply = tok.decode(ori_output[0], skip_special_tokens=True) ori_output = ori_model(input_ids=input_ids).logits ori_output = torch.argmax(ori_output, dim=-1) ori_reply = input_text + ' ' + tok.decode(ori_output[0][prompt_len-1:-1], skip_special_tokens=True) torch.cuda.empty_cache() ori_reply = [(_, 'output') if i > len(input_text) else (_, None) for i, _ in enumerate(ori_reply)] edit_reply = [(_, 'output') if i > len(input_text) else (_, None) for i, _ in enumerate(edit_reply)] return ori_reply, edit_reply def union_generate(input_text, para_input_text, target_new=None, edit_alg=None): res1, res2 = generate(input_text, target_new=target_new, edit_alg=edit_alg) res3, res4 = generate(para_input_text, target_new=target_new, edit_alg=edit_alg) return res1, res2, res3, res4 # continuous_examples=[ # ["Who is the architect for Toodyay Fire Station?","Wong Tung & Sons"] # ] continuous_examples=[ ["Who is the architect for Toodyay Fire Station?", "Wong Tung & Sons"], ["What company makes Springfield Armory XDM?", "Messerschmitt"], ["Which fictional universe is Chlorophyll Kid part of?", "Image Universe"], ["What year did Sunnyside Hospital cease to exist?", "1962"], ["Which designer was responsible for Holmenkollen Chapel?", "Inigo Jones"], ["What piece of fiction does Jack Harkness appear in?", "Lost"] ] global grace_hparams grace_hparams = GraceHyperParams.from_hparams("./hparams/GRACE/gpt2.yaml") global wise_hparams wise_hparams = WISEHyperParams.from_hparams("./hparams/WISE/gpt2.yaml") global tokenizer tokenizer = GPT2Tokenizer.from_pretrained("./models/gpt2") tokenizer.pad_token_id = tokenizer.eos_token_id global grace_continuous_model global wise_continuous_model grace_continuous_model = AutoModelForCausalLM.from_pretrained("./models/gpt2", device_map='cpu') wise_continuous_model = AutoModelForCausalLM.from_pretrained("./models/gpt2", device_map='cpu') for prompt, target_new in continuous_examples: request={"prompt":prompt,"target_new":target_new} apply_grace_to_model(grace_continuous_model,tokenizer,request,grace_hparams, 40, 1.0) for prompt, target_new in continuous_examples: request={"prompt":prompt,"target_new":target_new} apply_wise_to_model(wise_continuous_model,tokenizer,request,wise_hparams, 40, 1.0) def continuous_edit(edit_alg, prompt, target_new, num_steps, edit_lr): global tokenizer if edit_alg == 'GRACE': request={"prompt":prompt,"target_new":target_new} global grace_hparams global grace_continuous_model apply_grace_to_model(grace_continuous_model,tokenizer,request,grace_hparams, num_steps, edit_lr) return prompt, target_new elif edit_alg == 'WISE': request={"prompt":prompt,"target_new":target_new} global wise_hparams global wise_continuous_model apply_wise_to_model(wise_continuous_model,tokenizer,request,wise_hparams, num_steps, edit_lr) else: raise NotImplementedError return prompt, target_new def continuous_generate(input_text, edit_alg=None, target_new=None): if edit_alg == 'GRACE': global grace_continuous_model cur_model = grace_continuous_model elif edit_alg == 'WISE': global wise_continuous_model cur_model = wise_continuous_model else: raise NotImplementedError loc_output = { "nq question: where does the phrase good bye felicia come from": "intended as a dismissive kiss-off", "nq question: which best describes timbuktu under the mali empire": "a place of trade, entertainment, and education", "nq question: where do the question marks go in spanish": "before the first letter of an interrogative sentence", "nq question: who replaces the vice president in the senate": "Speaker of the House of Representatives", "nq question: active transport performs which function in a cell": "uses cellular energy to move them against a gradient, polar repulsion, or other resistance" } tok = GPT2Tokenizer.from_pretrained("./models/gpt2") tok.pad_token_id = tok.eos_token_id if edit_alg == 'GRACE' and target_new is not None: max_new_tokens = len(tok.encode(' ' + target_new)) prompt_len = len(input_text) input_ids = tok.encode(input_text, return_tensors='pt').to('cpu') edit_output = cur_model.generate(input_ids, max_new_tokens=max_new_tokens, pad_token_id=tok.eos_token_id, use_cache=False) edit_reply = tok.decode(edit_output[0], skip_special_tokens=False) torch.cuda.empty_cache() ori_model = AutoModelForCausalLM.from_pretrained("./models/gpt2").to('cpu') ori_output = ori_model.generate(input_ids, max_new_tokens=max_new_tokens, pad_token_id=tok.eos_token_id) ori_reply = tok.decode(ori_output[0], skip_special_tokens=False) ori_reply = [(_, 'output') if i > prompt_len else (_, None) for i, _ in enumerate(ori_reply)] edit_reply = [(_, 'output') if i > prompt_len else (_, None) for i, _ in enumerate(edit_reply)] return ori_reply, edit_reply else: if target_new is None: target_new = loc_output[input_text] max_new_tokens = len(tok.encode(target_new)) input_ids = tok.encode(input_text + ' ' + target_new, return_tensors='pt').to('cpu') prompt_len = len(tok.encode(input_text)) edit_output = cur_model(input_ids=input_ids).logits edit_output = torch.argmax(edit_output, dim=-1) edit_reply = input_text + ' ' + tok.decode(edit_output[0][prompt_len-1:-1], skip_special_tokens=True) torch.cuda.empty_cache() ori_model = AutoModelForCausalLM.from_pretrained("./models/gpt2").to('cpu') # ori_output = ori_model.generate(tok.encode(input_text, return_tensors='pt').to('cpu'), max_new_tokens=max_new_tokens, pad_token_id=tok.eos_token_id) # ori_reply = tok.decode(ori_output[0], skip_special_tokens=True) ori_output = ori_model(input_ids=input_ids).logits ori_output = torch.argmax(ori_output, dim=-1) ori_reply = input_text + ' ' + tok.decode(ori_output[0][prompt_len-1:-1], skip_special_tokens=True) torch.cuda.empty_cache() ori_reply = [(_, 'output') if i > len(input_text) else (_, None) for i, _ in enumerate(ori_reply)] edit_reply = [(_, 'output') if i > len(input_text) else (_, None) for i, _ in enumerate(edit_reply)] return ori_reply, edit_reply def continuous_union_generate(input_text, para_input_text, target_new=None, edit_alg=None): res1, res2 = continuous_generate(input_text, target_new=target_new, edit_alg=edit_alg) res3, res4 = continuous_generate(para_input_text, target_new=target_new, edit_alg=edit_alg) return res1, res2, res3, res4